Early applications of deep learning-based methods for speech enhancement employed fully connected neural networks (FNNs) [1]}, [2]}, [3]}, [4]} and recurrent neural networks (RNNs), in particular long short-term memory networks (LSTMs) [5]}, [6]}, [7]}.
Later, convolutional neural network-based methods (CNN) were introduced, first in the time-frequency domain [8]}, [9]}, [10]} and then in the time domain, for instance Denoising WaveNet [11]}, an adaption of the eponymous WaveNet, and SEGAN [12]}, [13]}, [14]}, which used a generative adversarial network (GAN) [15]}.
